Mapping the Cellular Response to Small Molecules Using
Chemogenomic Fitness Signatures
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Citation
Lee, A. Y., R. P. St.Onge, M. J. Proctor, I. M. Wallace, A. H. Nile,
P. A. Spagnuolo, Y. Jitkova, et al. “Mapping the Cellular
Response to Small Molecules Using Chemogenomic Fitness
Signatures.” Science 344, no. 6180 (April 10, 2014): 208–211.
As Published
http://dx.doi.org/10.1126/science.1250217
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American Association for the Advancement of Science (AAAS)
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Author's final manuscript
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Wed May 25 22:41:24 EDT 2016
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http://hdl.handle.net/1721.1/96521
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Published in final edited form as:
Science. 2014 April 11; 344(6180): 208–211. doi:10.1126/science.1250217.
Mapping the Cellular Response to Small Molecules Using
Chemogenomic Fitness Signatures
NIH-PA Author Manuscript
Anna Y. Lee1,2,*, Robert P. St.Onge3,*, Michael J. Proctor3, Iain M. Wallace1, Aaron H. Nile4,
Paul A. Spagnuolo5, Yulia Jitkova6, Marcela Gronda6, Yan Wu6, Moshe K. Kim7,8, Kahlin
Cheung-Ong1,2, Nikko P. Torres1,7, Eric D. Spear9, Mitchell K. L. Han10, Ulrich Schlecht3,
Sundari Suresh3, Geoffrey Duby11, Lawrence E. Heisler1, Anuradha Surendra1, Eula Fung3,
Malene L. Urbanus2, Marinella Gebbia1, Elena Lissina1,2, Molly Miranda3, Jennifer H.
Chiang12, Ana Maria Aparicio3, Mahel Zeghouf13, Ronald W. Davis3, Jacqueline Cherfils13,
Marc Boutry11, Chris A. Kaiser9, Carolyn L. Cummins10, William S. Trimble7,8, Grant W.
Brown1,7, Aaron D. Schimmer6, Vytas A. Bankaitis4, Corey Nislow1,2,12, Gary D. Bader1,2,
and Guri Giaever1,2,10,12,†
1The
Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
2Department
of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 1A8, Canada
3Department
of Biochemistry, Stanford Genome Technology Center, Stanford University, Palo
Alto, CA 94304, USA
4Department
of Molecular and Cellular Medicine, College of Medicine, Texas A&M University
Health Sciences Center, College Station, TX 77843–1114, USA
5School
of Pharmacy, University of Waterloo, Kitchener, Ontario N2G 1C5, Canada
6Princess
Margaret Cancer Centre, University Health Network, Toronto, Ontario M5G 2M9,
Canada
7Department
of Biochemistry, University of Toronto, Toronto, Ontario M5S 1A8, Canada
8Cell
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Biology Program, Hospital for Sick Children, University of Toronto, Toronto, Ontario M5G
1X8, Canada
9Department
of Biology, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
10Department
of Pharmaceutical Sciences, Faculty of Pharmacy, University of Toronto, Toronto,
Ontario M5S 3M2, Canada
Copyright 2014 by the American Association for the Advancement of Science; all rights reserved.
†
Corresponding author. g.giaever@ubc.ca.
*These authors contributed equally to this work.
The data reported in this paper are tabulated in the supplementary materials, and access to the entire data set is available for query and
in a variety of downloadable formats at http://chemogenomics.pharmacy.ubc.ca/HIPHOP/. The raw microarray data are archived in
the ArrayExpress database (www.ebi.ac.uk/arrayexpress) under accession no. E-MTAB-2391.
Supplementary Materials
www.sciencemag.org/content/344/6180/208/suppl/DC1
Materials and Methods
Figs. S1 to S24
Tables S1 to S10
References (24–65)
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11Institut
des Sciences de la Vie, Université catholique de Louvain, B-1348 Louvain-la-Neuve,
Belgium
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12Faculty
of Pharmaceutical Sciences, The University of British Columbia, Vancouver, British
Columbia, V6T 1Z3, Canada
13Laboratoire
d'Enzymologie et Biochimie Structurales, Centre National de la Recherche
Scientifique (CNRS), Gif-sur-Yvette, France
Abstract
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Genome-wide characterization of the in vivo cellular response to perturbation is fundamental to
understanding how cells survive stress. Identifying the proteins and pathways perturbed by small
molecules affects biology and medicine by revealing the mechanisms of drug action. We used a
yeast chemogenomics platform that quantifies the requirement for each gene for resistance to a
compound in vivo to profile 3250 small molecules in a systematic and unbiased manner. We
identified 317 compounds that specifically perturb the function of 121 genes and characterized the
mechanism of specific compounds. Global analysis revealed that the cellular response to small
molecules is limited and described by a network of 45 major chemogenomic signatures. Our
results provide a resource for the discovery of functional interactions among genes, chemicals, and
biological processes.
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Chemical genomics is a powerful approach for understanding in vivo mechanisms of drug
action. The ability to interpret molecular-level responses in a cellular context has led to
therapies for intractable diseases (1). “Guilt-byassociation” approaches allow mechanisms of
untested compounds to be inferred on the basis of profile similarity to established drugs (2,
3). Loss-of-function genetic screens provide direct mechanistic insight as they report genes
that when deleted, confer drug sensitivity. Here, we used yeast genomic tools (4) in loss-offunction assays to systematically characterize the cellular response to small-molecule
perturbation by screening 3250 compounds using a haploinsufficiency profiling (HIP) and
homozygous profiling (HOP) chemogenomic platform (5–7). HIP exploits drug-induced
haploinsufficiency (8), as measured by a growth or fitness defect (FD) observed in a
heterozygous strain deleted for one copy of the drug’s target gene. HIP identifies candidate
protein targets by measuring the drug-induced FDs of ~1100 heterozygous strains
representing the yeast essential genome (5, 6). In the complementary HOP assay, druginduced FDs are reported for ~4800 homozygous deletion strains, identifying the
nonessential genes required to buffer the targeted pathways (7, 9). Each combined HIPHOP
profile provides a genome-wide view of the cellular response to a specific compound.
By prescreening 50,000 diverse druglike small molecules, we identified 3250 compounds
that inhibited wild-type yeast growth (~95% of unknown mechanism; table S1 and fig. S1).
Each compound was profiled genome-wide, and FDs were measured for each strain; larger
scores representing a greater requirement for the deleted gene to resist chemical treatment
(10). For example, the Erg11Δ/ERG11 strain represents a “hit” as it had the largest FD in the
fluconazole HIP profile and passed significance and specificity thresholds (10). Fluconazole
inhibits the protein Erg11, thus demonstrating the ability of HIP to identify targets in vivo
(Fig. 1A). Fluconazole HOP identified mechanisms that buffer the ergosterol pathway,
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including the requirement for iron (Fig. 1A). Gene Ontology (GO) enrichments are provided
for each profile (fig. S2) (10). Additional relationships among genes, profiles, pathways, and
compounds can be explored with the interactive online HIPHOP chemogenomic database
http://chemogenomics.pharmacy.ubc.ca/HIPHOP/ (10).
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In total, HIP identified 317 compounds that specifically perturb the function of 121 essential
genes. To distinguish these compounds from drugs or credentialed chemical probes, we refer
to them as “chemical-genetic probes,” and to their interacting gene partners as “HIP hits”
(10). Consistent with the ability of HIP to identify protein targets, these specific interactions
were significantly enriched for established compound-target pairs (hypergeometric test P <
10−4) including drugs approved by the U.S. Food and Drug Administration (e.g., rapamycin)
and chemical probes (e.g., cerulenin) (table S2 and fig. S3). These drugs and probes target
homologous proteins in yeast and mammalian cells, suggesting that some of our
uncharacterized compounds may function similarly in mammalian cells, even though yeast
required about fives times as much compound to inhibit growth by 20% [minimum 20%
inhibitory concentration (IC20) = ~244 nM, median = ~100 µM; fig. S4 and table S3]. This
observation is consistent with published data (11, 12) and reflective of yeast’s robust
xenobiotic defenses. Using quantitative growth assays (fig. S5 and table S2), we confirmed
dose-dependent drug-induced haploinsufficiency for 63 compound-gene pairs, 54 of them
novel (figs. S6 to S9). Specific chemical-genetic probes were tested for inhibitory activities
in cell-free assays (IC50 range 1 to 500 µM, median = ~23 µM) and/or cell-based assays
(IC50 range 30 nM to 100 mM, median = 60 µM). For example, we validated inhibitors of
actin (0136-0228) and tubulin (1327-0036) in yeast and mammalian cell-based assays (IC50
range 30 nM to 100 µM) and in in vitro polymerization assays (IC50 range20 to25µM;
fig.S10).An in vitro assay suggested that compound 1327-0036 binds to the colchicinebinding site on the tubulin dimer (fig. S10). Another compound (3013-0144) perturbed the
septin Cdc12 (IC50 = 1 µM), exhibiting about five times more activity than forchlorfenuron,
a known septin inhibitor (13) (Fig. 1B). Three compounds that perturb Sec14, a conserved
phosphatidylinositol transfer protein, were also biochemically validated (IC50= ~1 µM; Fig.
1C), and we have recently shown that two additional inhibitors are effective in vivo and in
vitro (14). The specificity of these compounds for Sec14 was further validated by
demonstrating that suppressed double mutants cki1Δ sec14Δ (15) and kes1Δ sec14Δ (16)
were resistant to these inhibitors (fig. S7). This experimental support provides encouraging
proof-of-concept data and underscores the need for further characterization of putative
protein inhibitors (see fig. S5 for the structures of all validated inhibitors).
Hierarchically clustering all HIPHOP profiles allowed classification of cellular response
types into major (covering ~36% of profiles), minor (~40%), or unique (~24%) signatures.
Each major response was defined by a characteristic gene signature in a cluster of more than
four profiles, while most minor signatures were associated with three to four profiles (fig.
S11 and table S4). Several minor signatures point to compelling biology, including a
signature representing the response to three chemical-genetic probes that share a 2,5dimethylpyrrole chemical moiety and putatively target the geranylgeranyltransferase
complex RAM2/CDC43 (table S2). Unique signatures (one to two profiles each) include
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distinctive drugs (e.g., methotrexate) and chemical-genetic probes (e.g., 0kpi-0099, fig.
S12).
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We focused on the highest-confidence major cellular response signatures, which represent
~70% of our chemical-genetic probes (10). Of these 45 signatures, 33 were enriched for
known gene function, 40 represent ≥1 HIP hits, and 11 represent ≥2 compounds of known
mechanism (Fig. 2). Five of these 11 signatures are enriched for compounds with similar
bioactivity [hypergeometric test false discovery rate (FDR) ≤ 0.1; table S5], supporting
mechanism prediction for related compounds (10) (Fig. 2). For example, the exosome
signature compounds included four chemotherapeutics known to target this complex
(hypergeometric test P < 10−10; Fig. 3) (5, 6), allowing a similar mechanism to be inferred
for two additional compounds. Similarly, we predict DNA damage as the underlying
mechanism for 20 uncharacterized compounds with the same signature as established DNAdamaging agents (table S5). Response signatures also provide hypotheses applicable to
mammalian cells. For example, trichlorophene induced mitochondrial stress.
Trichlorophene-treated immortalized human leukemia cells confirmed a mitochondrialspecific mechanism; exhibiting increased generation of mitochondrial reactive oxygen
species and a reduction in reserve oxygen capacity (fig. S13). Signatures also yielded new
information about well-characterized compounds; e.g., the tubulin inhibitors nocodazole and
benomyl induced a signature containing tubulin biogenesis and SWR1 complex genes, a
biological link between cytoarchitecture and chromatin structure supported by genetic
interaction data (17).
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Our signatures are recognizable in other genome-wide data sets, supporting their biological
relevance. Yeast large-scale genetic interaction data (18) revealed that our response
signatures were observed in 12% of 380 genetic profiles that lacked GO enrichment (table
S6). In some cases, the signatures provided annotation for uncharacterized genes. For
example, genes known to genetically interact with YPL109C (18) are not enriched for any
GO-based function, yet were significantly enriched for our ubiquinone biosynthesis and
proteasome signature (hypergeometric test FDR ≤ 0.1), suggesting a related function. Our
signatures also identify links between biological processes (fig. S14). For example, the
ubiquinone biosynthesis and proteasome signature links these two processes by 38 gene
pairs exhibiting correlated fitness, or “cofitness.” Independent support for this observation is
provided by nine genetic interactions (18) and one physical interaction (17) (table S7).
Cofitness also supported a functional relationship between diphthamide biosynthesis and
histone exchange in the NEO1-PIK1 signature (table S7).
Approximately half (n = 28) of the major response signatures are associated with
compounds significantly enriched for chemical moieties (hypergeometric test FDR ≤ 0.1;
table S8 and Fig. 2), suggesting that specific molecular structural properties can drive a
cellular response. For example, the NEO1 and NEO1-PIK1 signatures (Fig. 2) are
characterized by NEO1 haploinsufficiency induced by cationic amphiphilic drugs (CADs;
fig. S15). CADs are associated with drug-induced phospholipidosis (DIPL), a human
phospholipid storage disorder (19, 20) caused by diverse therapeutics. At a cellular level,
DIPL arises from the selective accumulation of CADs in the acidic vacuole and lysosome, in
yeast and mammalian cells, respectively. Consistent with published yeast genetic studies, we
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confirmed that inhibition of yeast vacuolar adenosine triphosphatase by bafilomycin A
alleviates the FD induced by CADs (21). Furthermore, we found that bafilomycin A rescued
CAD-induced NEO1 haploinsuffiency (Fig. 4A and fig. S8). Structural features of NEO1
and NEO1-PIK1 compounds proved predictive of response; a statistical structure-based
model performed about seven times better than random in identifying compounds that
induce NEO1 haploinsufficiency (fig. S16; percentage of correct predictions in crossvalidation = 99%) (10). Our yeast-based model suggests that haploinsufficiency of NEO1,
and by extension, its human homologs (ATP9A and B), may prove useful as a biomarker to
identify DIPL-causing compounds (Fig. 4B).
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Our systems-level view of the cellular response to small molecules provides a resource for
the exploration of multifaceted relationships among genes, biological processes, chemical
structures, and response signatures. Although not previously captured by any existing GO
category, in retrospect, we detect signatures present in other large-scale genomic data sets
suggesting that they may be used to address the challenges of incomplete gene annotation
and integration of diverse genome-wide data sets. It is likely that we have identified all
major signatures (within similar chemical space in yeast), as we observed saturation in our
screen. Reanalysis of our prior chemogenomic data set (7) revealed that ~60% of the 45
signatures could be detected (fig. S17 and table S4), and simulation demonstrates that 80%
of our 45 major clusters would be identified after screening <30% of the compounds (fig.
S18). We expect that these signatures therefore represent fundamental small-molecule
response systems that are present across eukaryotic cells. Accordingly, we expect that many
of our 317 chemical-genetic probes will be directly applicable to mammalian cell biology
and may support novel targets as opportunities to pursue for therapeutic intervention (5, 22,
23).
Supplementary Material
Refer to Web version on PubMed Central for supplementary material.
Acknowledgments
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Supported by the National Human Genome Research Institute (RO1 003317-07) (C.N., G.G., and R.W.D.);
Canadian Cancer Society Research Institute (#020380) (C.N. and G.G.); Canadian Institute for Health Research
(MOP-81340) (G.G.), MOP-79368 (G.W.B.), and MOP-700724 (W.S.T.); Canadian Research Chair (G.G.);
Charles H. Best Institute (A.Y.L.); Belgian National Fund for Scientific Research and the Interuniversity Poles of
Attraction Program (M.B.); French National Research Agency (J.C.); Marie Curie Fellowship (I.M.W.); Leukemia
and Lymphoma Society (A.D.S.); Robert A. Welch Foundation (V.A.B.); and NIH grants (GM103504) (G.D.B.)
and GM44530 (V.A.B.).
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Fig. 1. Validation of chemical-genetic probes
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(A) Fluconazole HIPHOP profile. Fitness defect (FD) scores plotted for each deletion strain.
HIP (left) identifies the established drug target Erg11. HOP (right) identifies processes
directly (e.g., sterol biosynthesis) and indirectly (e.g., iron ion homeostasis) related to
ERG11 function. Significant FDs (standard normal distribution P ≤ 0.001) are labeled
except those (blue) not covered by the highlighted processes; *, dubious gene overlapping
labeled gene. (B) Cdc12 inhibitor. In a wound-healing assay, HeLa cells with dimethyl
sulfoxide (DMSO), 1 µM 3013-0144, and 5 µM forchlorfenuron (FCF) were fixed and
stained as described, with DNA stained blue and antibodies against the Golgi visualized via
green fluorescence (10). DMSO-treated cells show the Golgi reoriented toward the wound
edge (white line); in contrast, 3013-0144 inhibited Golgi reorientation as effectively as FCF
(scale bar, 10 µm). (C) Dose-dependent inhibition of the phosphatidylinositol (PtdIns)
transfer activity of purified recombinant Sec14 (10). Transfer of radiolabeled PtdIns as a
percentage of the untreated control (y axis), measured in the presence of 9131112, 9097855,
9053361*, and 9045654 (an inactive derivative) at the indicated concentrations (x axis).
Data are mean ± SD (N = 3). *9053361 did not qualify as a HIP hit, but was nonetheless
validated.
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Fig. 2. The cellular response is defined by a network of chemogenomic response signatures
Each circular node represents a major signature; size is proportional to confidence in the
signature (10). Node color: dark blue if GO enriched (hypergeometric test FDR ≤ 0.1), pale
blue otherwise. Node border color: green, signature represents two or more compounds of
known mechanism (select compound names are shown, and are in bold if they drive
bioactivity class enrichment); red, signature represents chemical-genetic probes (select HIP
hits are shown, and are in bold if validation data are provided). Signatures are connected to
chemical moiety nodes where signature compounds are enriched (hypergeometric test FDR
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≤ 0.1). Boxes indicate signatures discussed in the text. ERAD, endoplasmic reticulum–
associated degradation; RNA pol III, RNA polymerase III; ROS, reactive oxygen species;
TOFA, 5-tetradecoxyfuran-2-carboxylic acid; TRP, tryptophan.
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Fig. 3. Mechanism inference with the exosome signature
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(A) Dendrogram of the exosome profiles extracted from the dendrogram of all HIPHOP
profiles. Profiled compounds with established mechanisms are shown in red. (B) The
exosome signature. For each gene in the signature, the bar plot indicates the median FD
score across the exosome profiles. (C) Mechanism inferred by signature similarity. Scores of
genes exhibiting significant fitness defects in the profiles of 5-fluorouridine and an
uncharacterized compound (4215-0184) associated with the exosome signature. Both
compounds contain a 5-fluoropyrimidine substructure (green). Guilt-by-association infers
that 4215-0184 inhibits the exosome.
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Fig. 4. NEO1-based signatures and drug-induced phospholipidosis (DIPL)
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(A) Rescue of CAD-induced NEO1 haploinsufficiency. Tamoxifen-induced NEO1
haploinsufficiency is rescued by bafilomycin A. Growth of the neo1Δ/NEO1 strain treated
with these compounds was monitored by measuring the optical density at 600 nm (OD600) (y
axis) for 24 hours (x axis). (B) Prediction of DIPL. The plot shows the percentage of DIPLcausing compounds (actives; y axis) identified among the top-scoring compounds (x axis).
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